AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm
Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2024-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2300 |
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| _version_ | 1849319261029269504 |
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| author | CHEN Xiufeng WANG Chengxin WU Yuechen GU Kexin |
| author_facet | CHEN Xiufeng WANG Chengxin WU Yuechen GU Kexin |
| author_sort | CHEN Xiufeng |
| collection | DOAJ |
| description |
Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. For the problem of heterogeneous redundant frames，weighted non-maximum value suppression is used to fuse the information of both frames to improve the detection accuracy. The experimental results show that the average detection accuracy ( mAP @ 0. 5 ∶ 0. 95 ) of the improved YOLOv5s algorithm reaches 64. 17% . Compared with the YOLOv5s algorithm，the precision and recall rate are improved by 1. 72% and 0. 72% respectively. In the small target vehicle detection，the positive detection rate is increased by 5. 95% and the missed detection rate is reduced by 4. 63% . The improved YOLOv5s algorithm can effectively improve the detection precision and accuracy of small target vehicles. |
| format | Article |
| id | doaj-art-580cf0c4ab63459caa1fb0d7d15a0ed2 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-580cf0c4ab63459caa1fb0d7d15a0ed22025-08-20T03:50:32ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-02-01290110711410.15938/j.jhust.2024.01.012AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s AlgorithmCHEN Xiufeng0WANG Chengxin1WU Yuechen2GU Kexin3School of Civil Engineering，Qingdao University of Technology，Qingdao 266520，ChinaSchool of Civil Engineering，Qingdao University of Technology，Qingdao 266520，ChinaSchool of Civil Engineering，Qingdao University of Technology，Qingdao 266520，ChinaSchool of Civil Engineering，Qingdao University of Technology，Qingdao 266520，China Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. For the problem of heterogeneous redundant frames，weighted non-maximum value suppression is used to fuse the information of both frames to improve the detection accuracy. The experimental results show that the average detection accuracy ( mAP @ 0. 5 ∶ 0. 95 ) of the improved YOLOv5s algorithm reaches 64. 17% . Compared with the YOLOv5s algorithm，the precision and recall rate are improved by 1. 72% and 0. 72% respectively. In the small target vehicle detection，the positive detection rate is increased by 5. 95% and the missed detection rate is reduced by 4. 63% . The improved YOLOv5s algorithm can effectively improve the detection precision and accuracy of small target vehicles.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2300vehicle detectiondeep learningthe improved yolov5 algorithmsmall target detectionheterogeneous redundant frames |
| spellingShingle | CHEN Xiufeng WANG Chengxin WU Yuechen GU Kexin AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm Journal of Harbin University of Science and Technology vehicle detection deep learning the improved yolov5 algorithm small target detection heterogeneous redundant frames |
| title | AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm |
| title_full | AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm |
| title_fullStr | AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm |
| title_full_unstemmed | AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm |
| title_short | AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm |
| title_sort | areal time detection method of vehicle target based on improved yolov5s algorithm |
| topic | vehicle detection deep learning the improved yolov5 algorithm small target detection heterogeneous redundant frames |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2300 |
| work_keys_str_mv | AT chenxiufeng arealtimedetectionmethodofvehicletargetbasedonimprovedyolov5salgorithm AT wangchengxin arealtimedetectionmethodofvehicletargetbasedonimprovedyolov5salgorithm AT wuyuechen arealtimedetectionmethodofvehicletargetbasedonimprovedyolov5salgorithm AT gukexin arealtimedetectionmethodofvehicletargetbasedonimprovedyolov5salgorithm |